Preference Data Mining And Its Application Based On Big Data

With the wide application of information technology in education, rich teaching data is produced. So how to dig out and analyze the information that is benefit to education and management is the research hotspot at present.All kinds of data contain a great mass of useful information to improve the teaching activities, with the accumulation of teaching data, and then a huge teaching database is formed, however, data mining is to find the valuable ones in the numerous mass data, as the preference data have high value, its excavation will play an important role on teaching quality.According to massive data in teaching accumulation,we mining and analyze these data.Firstly, the paper studies and analyzes the status quo of preference data mining based on large data, then discussed the mining principle and technology. Thirdly, construct data mining model to construct user preference, preference learning model based on K-means clustering and association rules algorithm. Finally, the design and implementation of autonomous learning platform, under the support of the platform, complete the mining task. To mining in depth preference data of the student is conducive to teach students in accordance with their aptitude. The main content of this paper include the following aspects:(1) Analysis of the current situation of preference of data mining, and then put forward the problem of preference data mining based on large data. On this basis, discussed the mining principle and technology.(2) It constructs and designs the students’ preference structure by using ontology technology, which is explained through OWL language.(3) Divide the students’ learning interests into two parts: the short term one and long term one, and come up with a students’ learning preference model that is based on the big data. According to students’ browsing behaviors and logging data, the short term learning interests can be excavated. Make use of the data in background server database to excavate the registration information of the initial students, and then the long term learning interests can be extracted.(4) Design the algorithm of association rules and the K-MEANS clustering algorithm to dig out the valuable information from browsing the logging records and information feedback to learn the students’ learning preferences, build the association and make the sorting treatment.(5) Build big data platform which based on the framework of autonomous learning, finally, it can achieve the main modules on the platform.